The need to detect and track targets (e.g., runways, landing strips, landing pads, roads, obstacles, objects, landmarks, and the like) from moving platforms is driving the development of sensor fusion and computer vision algorithms for next-generation situational awareness and navigation systems. For example, landing an aircraft, whether an unmanned air vehicle (UAV) or manned vehicle, requires accurate information about the location of the runway. Similarly, military targeting systems for urban environments require accurate localization to decrease or avoid altogether collateral damage and civilian casualties.
The navigation data extracted from multiple databases of current navigation data sources is not sufficient to accurately position an aircraft or other vehicle (e.g., motor vehicles, military vehicles, etc.) in relation to a target. Specifically, the resolution available using such navigation data sources is generally measured in meters, which may be too large a measurement for many purposes. That is, a low precision guidance system may become hampered by severe weather conditions, which may present a potential challenge for safely landing an aircraft, navigating motor vehicles along a terrestrial route, and/or tracking targets of interest.
With advances in computer vision and the increases in computing power, there exists a desire for the inclusion of accurate vision sensors as a major component in navigation and target positioning systems. Current vision sensors are designed to analyze sensor data from a moving platform in order to provide pilots or vehicle operators with additional features that, for example, enable pilots/operators to navigate aircraft or other vehicles, identify and avoid potential hazards/imminent threats, and/or obtain sufficient visual reference of the actual runway and or other target. In creating such systems, designers have had to deal several shortcomings including, for example, competing background clutter, changing background dynamics, noise, and/or other problems created by the motion/movement of the aircraft or vehicle.
On the other hand, the detection of targets is generally carried out through the implementation of segmentation and tracking techniques based on image correlation as maintained by a static platform hypothesis or static scene hypothesis, and subsequently, by analyzing the resultant sequence of frames independently. When there are significant changes in the scene or the target is moving rapidly, this type of analysis usually results in jitter due to incomplete motion compensation from the sensor and/or the platform.
Accordingly, there is a desire to provide systems and methods for recognizing the location of a target by analyzing real-time sensor images from moving platforms while compensating for the motion of the aircraft or vehicle. Furthermore, other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description of the invention and the appended claims, taken in conjunction with the accompanying drawings and this background of the invention.